
# Installing the libraries with the specified version.
# !pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user


# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

# Library to split data
from sklearn.model_selection import train_test_split

# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV


# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    precision_recall_curve,
    roc_curve,
    make_scorer,
)

import warnings
warnings.filterwarnings("ignore")

from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter("ignore", ConvergenceWarning)


from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).


dataset = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/INNHotelsGroup.csv')
dataset.head(10)


# checking number of rows and columns
dataset.shape

(36275, 19)


# checking information, data types
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB


# checking null values
dataset.isnull().sum()


# checking duplicates
dataset.duplicated().sum()

0


# copying data to another variable to avoid any changes to original data
data = dataset.copy()


# booking is unique and irrelavant for analysis
data = data.drop('Booking_ID', axis=1)


data.describe().T


data.head()


# function to create boxplot and histogram combined
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (10,7))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color='blue'
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="red", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


histogram_boxplot(data, 'avg_price_per_room')


data[data["avg_price_per_room"] == 0]


data.loc[data["avg_price_per_room"] == 0, "market_segment_type"].value_counts()


# Calculating the 25th and 75th quantile
Q1 = data["avg_price_per_room"].quantile(0.25)
Q3 = data['avg_price_per_room'].quantile(0.75)

# Calculating IQR
IQR = Q3 - Q1

# Calculating value of upper whisker
Upper_Whisker = Q3 + 1.5 * IQR
Upper_Whisker

179.55


# assigning the outliers the value of upper whisker
data.loc[data["avg_price_per_room"] >= 500, "avg_price_per_room"] = Upper_Whisker


histogram_boxplot(data, 'lead_time')


histogram_boxplot(data, 'no_of_previous_cancellations')


histogram_boxplot(data, 'no_of_previous_bookings_not_canceled')


histogram_boxplot(data, 'no_of_special_requests')


histogram_boxplot(data, 'no_of_week_nights')


histogram_boxplot(data, 'no_of_weekend_nights')


histogram_boxplot(data, 'required_car_parking_space')


#Observation on repeated guests
histogram_boxplot(data, 'repeated_guest')


histogram_boxplot(data, 'no_of_adults')


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


labeled_barplot(data, 'no_of_adults', perc=True)


labeled_barplot(data, 'no_of_children', perc=True)


data['no_of_children'] = data['no_of_children'].replace([9, 10], 3)


labeled_barplot(data, 'no_of_weekend_nights', perc=True)


labeled_barplot(data, 'no_of_week_nights', perc=True)


# customers require car parking space
labeled_barplot(data, 'required_car_parking_space', perc=True)


# meal plan preferance
labeled_barplot(data, 'type_of_meal_plan', perc=True)


# Room type preferance
labeled_barplot(data, 'room_type_reserved', perc=True)


# Arrival year distribution
labeled_barplot(data, 'arrival_year', perc=True)


# Arrival month distribution
labeled_barplot(data, 'arrival_month', perc=True)


# market segment type
labeled_barplot(data, 'market_segment_type', perc=True)


# repeated guests distribution
labeled_barplot(data, 'repeated_guest', perc=True)


# Special requests
labeled_barplot(data, 'no_of_special_requests', perc=True)


# booking status
labeled_barplot(data, 'booking_status', perc=True)


# select numerical columns
cols_list = data.select_dtypes(include=np.number).columns.tolist()

# heatmap
plt.figure(figsize=(12, 7))
sns.heatmap(
    data[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="coolwarm"
)
plt.show()


### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0])

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
    )

    plt.tight_layout()
    plt.show()


def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


stacked_barplot(dataset, "market_segment_type", "booking_status")

booking_status       Canceled  Not_Canceled    All
market_segment_type                               
All                     11885         24390  36275
Online                   8475         14739  23214
Offline                  3153          7375  10528
Corporate                 220          1797   2017
Aviation                   37            88    125
Complementary               0           391    391
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(dataset, "room_type_reserved", "booking_status")

booking_status      Canceled  Not_Canceled    All
room_type_reserved                               
All                    11885         24390  36275
Room_Type 1             9072         19058  28130
Room_Type 4             2069          3988   6057
Room_Type 6              406           560    966
Room_Type 2              228           464    692
Room_Type 5               72           193    265
Room_Type 7               36           122    158
Room_Type 3                2             5      7
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(dataset, "type_of_meal_plan", "booking_status")

booking_status     Canceled  Not_Canceled    All
type_of_meal_plan                               
All                   11885         24390  36275
Meal Plan 1            8679         19156  27835
Not Selected           1699          3431   5130
Meal Plan 2            1506          1799   3305
Meal Plan 3               1             4      5
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(dataset, "arrival_month", "booking_status")

booking_status  Canceled  Not_Canceled    All
arrival_month                                
All                11885         24390  36275
10                  1880          3437   5317
9                   1538          3073   4611
8                   1488          2325   3813
7                   1314          1606   2920
6                   1291          1912   3203
4                    995          1741   2736
5                    948          1650   2598
11                   875          2105   2980
3                    700          1658   2358
2                    430          1274   1704
12                   402          2619   3021
1                     24           990   1014
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(dataset, "repeated_guest", "booking_status")

booking_status  Canceled  Not_Canceled    All
repeated_guest                               
All                11885         24390  36275
0                  11869         23476  35345
1                     16           914    930
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(dataset, "required_car_parking_space", "booking_status")

booking_status              Canceled  Not_Canceled    All
required_car_parking_space                               
All                            11885         24390  36275
0                              11771         23380  35151
1                                114          1010   1124
------------------------------------------------------------------------------------------------------------------------


distribution_plot_wrt_target(data, 'avg_price_per_room', 'booking_status')


distribution_plot_wrt_target(data, 'no_of_previous_cancellations', 'booking_status')


distribution_plot_wrt_target(data, 'no_of_previous_bookings_not_canceled', 'booking_status')


distribution_plot_wrt_target(data, 'no_of_week_nights', 'booking_status')


distribution_plot_wrt_target(dataset, 'no_of_weekend_nights', 'booking_status')


distribution_plot_wrt_target(dataset, 'required_car_parking_space', 'booking_status')


distribution_plot_wrt_target(dataset, 'repeated_guest', 'booking_status')


# What are the busiest months in the hotel?
dataset['arrival_month'].value_counts()


plt.figure(figsize=(10, 6))
sns.countplot(data=dataset, x='arrival_month')
plt.title('Busiest Months in the Hotel')
plt.xlabel('Month')
plt.ylabel('Count')
plt.show()


# Which market segment do most of the guests come from
dataset['market_segment_type'].value_counts()


plt.figure(figsize=(10, 6))
sns.countplot(data=dataset, x='market_segment_type')
plt.title('Market Segment Type')
plt.xlabel('Market Segment Type')
plt.ylabel('Count')

Text(0, 0.5, 'Count')


# Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?

difference_in_room_prices = dataset.groupby('market_segment_type')['avg_price_per_room'].mean()
print(difference_in_room_prices)

market_segment_type
Aviation        100.70400
Complementary     3.14176
Corporate        82.91174
Offline          91.63268
Online          112.25685
Name: avg_price_per_room, dtype: float64


# What percentage of bookings are canceled?
percent_bookings_cancelled = dataset['booking_status'].value_counts(normalize=True) * 100
print(percent_bookings_cancelled)

booking_status
Not_Canceled   67.23639
Canceled       32.76361
Name: proportion, dtype: float64


# Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?
repeating_guests = dataset[dataset['repeated_guest'] == 1]
percent_repeating_guests_cancelled = repeating_guests['booking_status'].value_counts(normalize=True) * 100
print(percent_repeating_guests_cancelled)

booking_status
Not_Canceled   98.27957
Canceled        1.72043
Name: proportion, dtype: float64


# Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?
special_requirements = dataset[dataset['no_of_special_requests'] > 0]
percent_special_requirements_cancelled = special_requirements['booking_status'].value_counts(normalize=True) * 100
print(percent_special_requirements_cancelled)

booking_status
Not_Canceled   79.75512
Canceled       20.24488
Name: proportion, dtype: float64


dataset.isnull().sum()


numeric_columns = dataset.select_dtypes(include=np.number).columns.tolist()

plt.figure(figsize=(15, 12))

for i, variable in enumerate(numeric_columns):
    plt.subplot(4, 4, i + 1)
    plt.boxplot(dataset[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


labeled_barplot(data, 'market_segment_type', perc=True)


labeled_barplot(data, 'booking_status', perc=True)


labeled_barplot(data, 'room_type_reserved', perc=True)


labeled_barplot(data, 'type_of_meal_plan', perc=True)


labeled_barplot(data, 'arrival_month', perc=True)


labeled_barplot(data, 'arrival_year', perc=True)


labeled_barplot(data, 'repeated_guest', perc=True)


labeled_barplot(data, 'required_car_parking_space', perc=True)


labeled_barplot(data, 'no_of_special_requests', perc=True)


labeled_barplot(data, 'no_of_week_nights', perc=True)


labeled_barplot(data, 'no_of_weekend_nights', perc=True)


labeled_barplot(data, 'no_of_previous_cancellations', perc=True)


num_cols = dataset.select_dtypes(include=np.number).columns

plt.figure(figsize=(12,7))
sns.heatmap(dataset[num_cols].corr(), annot=True, cmap='coolwarm', vmax=1, vmin=-1, fmt='.2f')
plt.show()


# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


data["booking_status"] = data["booking_status"].apply(lambda x: 1 if x == "Canceled" else 0)


# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


X = data.drop(["booking_status"], axis=1)
y = data["booking_status"]

# adding constant
X = sm.add_constant(X)

X = pd.get_dummies(X, drop_first=True)
X = X.astype(int)

# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.30, random_state=1
)


X_train.head()


y_train.head()


from sklearn.linear_model import LogisticRegression
logit = sm.Logit(y_train, X_train.astype(float))
lg = logit.fit(disp=False)
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25364
Method:                           MLE   Df Model:                           27
Date:                Fri, 04 Oct 2024   Pseudo R-squ.:                  0.3293
Time:                        23:37:27   Log-Likelihood:                -10792.
converged:                      False   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                 -921.5462    120.827     -7.627      0.000   -1158.362    -684.730
no_of_adults                             0.1145      0.038      3.040      0.002       0.041       0.188
no_of_children                           0.1579      0.062      2.542      0.011       0.036       0.280
no_of_weekend_nights                     0.1074      0.020      5.432      0.000       0.069       0.146
no_of_week_nights                        0.0403      0.012      3.281      0.001       0.016       0.064
required_car_parking_space              -1.5942      0.138    -11.565      0.000      -1.864      -1.324
lead_time                                0.0157      0.000     58.867      0.000       0.015       0.016
arrival_year                             0.4555      0.060      7.607      0.000       0.338       0.573
arrival_month                           -0.0417      0.006     -6.447      0.000      -0.054      -0.029
arrival_date                             0.0005      0.002      0.261      0.794      -0.003       0.004
repeated_guest                          -2.3481      0.617     -3.808      0.000      -3.557      -1.140
no_of_previous_cancellations             0.2665      0.086      3.108      0.002       0.098       0.435
no_of_previous_bookings_not_canceled    -0.1726      0.152     -1.132      0.258      -0.471       0.126
avg_price_per_room                       0.0188      0.001     25.454      0.000       0.017       0.020
no_of_special_requests                  -1.4688      0.030    -48.781      0.000      -1.528      -1.410
type_of_meal_plan_Meal Plan 2            0.1748      0.067      2.623      0.009       0.044       0.305
type_of_meal_plan_Meal Plan 3           17.3532   3982.075      0.004      0.997   -7787.371    7822.077
type_of_meal_plan_Not Selected           0.2772      0.053      5.225      0.000       0.173       0.381
room_type_reserved_Room_Type 2          -0.3582      0.131     -2.729      0.006      -0.615      -0.101
room_type_reserved_Room_Type 3          -0.0002      1.310     -0.000      1.000      -2.567       2.567
room_type_reserved_Room_Type 4          -0.2824      0.053     -5.307      0.000      -0.387      -0.178
room_type_reserved_Room_Type 5          -0.7175      0.209     -3.431      0.001      -1.127      -0.308
room_type_reserved_Room_Type 6          -0.9501      0.151     -6.276      0.000      -1.247      -0.653
room_type_reserved_Room_Type 7          -1.4000      0.294     -4.770      0.000      -1.975      -0.825
market_segment_type_Complementary      -40.5859   5.63e+05  -7.21e-05      1.000    -1.1e+06     1.1e+06
market_segment_type_Corporate           -1.1911      0.266     -4.477      0.000      -1.713      -0.670
market_segment_type_Offline             -2.1931      0.255     -8.613      0.000      -2.692      -1.694
market_segment_type_Online              -0.3934      0.251     -1.566      0.117      -0.886       0.099
========================================================================================================


print("Training performance:")
model_performance_classification_statsmodels(lg, X_train, y_train)

Training performance:


# we will define a function to check VIF
def checking_vif(predictors):
    vif = pd.DataFrame()
    vif["feature"] = predictors.columns

    # calculating VIF for each feature
    vif["VIF"] = [
        variance_inflation_factor(predictors.values, i)
        for i in range(len(predictors.columns))
    ]
    return vif


checking_vif(X_train).sort_values(by="VIF", ascending=False)


# initial list of columns
cols = X_train.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    x_train_aux = X_train.astype(float)[cols]

    # fitting the model
    model = sm.Logit(y_train, x_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)

['const', 'no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'repeated_guest', 'no_of_previous_cancellations', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Corporate', 'market_segment_type_Offline']


X_train1 = X_train[selected_features]
X_test1 = X_test[selected_features]


logit1 = sm.Logit(y_train, X_train1.astype(float))
lg1 = logit1.fit(disp=False)
print(lg1.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25392
Model:                          Logit   Df Residuals:                    25370
Method:                           MLE   Df Model:                           21
Date:                Fri, 04 Oct 2024   Pseudo R-squ.:                  0.3283
Time:                        23:37:31   Log-Likelihood:                -10809.
converged:                       True   LL-Null:                       -16091.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                           -914.2561    120.467     -7.589      0.000   -1150.368    -678.145
no_of_adults                       0.1097      0.037      2.939      0.003       0.037       0.183
no_of_children                     0.1530      0.062      2.468      0.014       0.031       0.275
no_of_weekend_nights               0.1093      0.020      5.535      0.000       0.071       0.148
no_of_week_nights                  0.0423      0.012      3.445      0.001       0.018       0.066
required_car_parking_space        -1.5947      0.138    -11.565      0.000      -1.865      -1.324
lead_time                          0.0157      0.000     59.218      0.000       0.015       0.016
arrival_year                       0.4516      0.060      7.565      0.000       0.335       0.569
arrival_month                     -0.0426      0.006     -6.598      0.000      -0.055      -0.030
repeated_guest                    -2.7378      0.557     -4.917      0.000      -3.829      -1.647
no_of_previous_cancellations       0.2289      0.077      2.982      0.003       0.078       0.379
avg_price_per_room                 0.0192      0.001     26.393      0.000       0.018       0.021
no_of_special_requests            -1.4697      0.030    -48.881      0.000      -1.529      -1.411
type_of_meal_plan_Meal Plan 2      0.1634      0.067      2.455      0.014       0.033       0.294
type_of_meal_plan_Not Selected     0.2848      0.053      5.385      0.000       0.181       0.388
room_type_reserved_Room_Type 2    -0.3528      0.131     -2.690      0.007      -0.610      -0.096
room_type_reserved_Room_Type 4    -0.2831      0.053     -5.334      0.000      -0.387      -0.179
room_type_reserved_Room_Type 5    -0.7350      0.208     -3.527      0.000      -1.143      -0.327
room_type_reserved_Room_Type 6    -0.9682      0.151     -6.404      0.000      -1.265      -0.672
room_type_reserved_Room_Type 7    -1.4341      0.293     -4.893      0.000      -2.009      -0.860
market_segment_type_Corporate     -0.7960      0.103     -7.738      0.000      -0.998      -0.594
market_segment_type_Offline       -1.7901      0.052    -34.451      0.000      -1.892      -1.688
==================================================================================================


print("Training performance:")
model_performance_classification_statsmodels(lg1, X_train1.astype(float), y_train.astype(float))

Training performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1.astype(float), y_train.astype(float))


print("Training performance:")
log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg1, X_train1.astype(float), y_train.astype(float)
)
log_reg_model_train_perf

Training performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1.astype(float), y_test.astype(float))


log_reg_model_test_perf = model_performance_classification_statsmodels(
    lg1, X_test1.astype(float), y_test.astype(float)
)

print("Test performance:")
log_reg_model_test_perf

Test performance:


# converting coefficients to odds
odds = np.exp(lg1.params)

# finding the percentage change
perc_change_odds = (np.exp(lg1.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train1.columns).T


logit_roc_auc_train = roc_auc_score(y_train, lg1.predict(X_train1))
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg1.predict(X_train1))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.372598551491902


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)


# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:


logit_roc_auc_train = roc_auc_score(y_test.astype(float), lg1.predict(X_test1.astype(float)))
fpr, tpr, thresholds = roc_curve(y_test.astype(float), lg1.predict(X_test1.astype(float)))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.01])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1.astype(float), y_test.astype(float), threshold=optimal_threshold_auc_roc)


# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_test1.astype(float), y_test.astype(float), threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:


y_scores = lg1.predict(X_train1)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()


# setting the threshold
optimal_threshold_curve = 0.42


# creating confusion matrix
confusion_matrix_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_curve
)


log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_train1, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1.astype(float), y_test.astype(float), threshold=optimal_threshold_curve)


log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_test1.astype(float), y_test.astype(float), threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# test performance comparison

models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Test performance comparison:")
models_test_comp_df

Test performance comparison:


log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_test1.astype(float), y_test.astype(float), threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_train1, y_train)


log_reg_model_test_perf = model_performance_classification_statsmodels(lg1, X_test1, y_test)

print("Test performance:")
log_reg_model_test_perf

Test performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold=optimal_threshold_curve)


# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg1, X_test1, y_test, threshold=optimal_threshold_curve)


log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg1, X_test1, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# test performance comparison
models_test_comp_df = pd.concat(
    [
        log_reg_model_test_perf.T,
        log_reg_model_test_perf_threshold_auc_roc.T,
        log_reg_model_test_perf_threshold_curve.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.37 Threshold",
    "Logistic Regression-0.42 Threshold",
]

print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:


X = data.drop('booking_status', axis=1)
y = data['booking_status']

X = pd.get_dummies(X, drop_first=True)
X = X.astype(int)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)


print("Shape of Training set : ", X_train.shape)
print("Shape of test set : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set :  (25392, 27)
Shape of test set :  (10883, 27)
Percentage of classes in training set:
booking_status
0   0.67238
1   0.32762
Name: proportion, dtype: float64
Percentage of classes in test set:
booking_status
0   0.67233
1   0.32767
Name: proportion, dtype: float64


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


dTree = DecisionTreeClassifier(random_state=1)
dTree.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


print('Training data Accuracy:', dTree.score(X_train, y_train))
print('Test data Accuracy:', dTree.score(X_test, y_test))

Training data Accuracy: 0.9943683049779458
Test data Accuracy: 0.8635486538638243


## Function to create confusion matrix
from sklearn import metrics
def make_confusion_matrix(model,y_actual,labels=[1, 0]):
    '''
    model : classifier to predict values of X
    y_actual : ground truth

    '''
    y_predict = model.predict(X_test)
    cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
    df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                  columns = [i for i in ['Predicted - No','Predicted - Yes']])
    group_counts = ["{0:0.0f}".format(value) for value in
                cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in
                         cm.flatten()/np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in
              zip(group_counts,group_percentages)]
    labels = np.asarray(labels).reshape(2,2)
    plt.figure(figsize = (10,7))
    sns.heatmap(df_cm, annot=labels,fmt='')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


##  Function to calculate recall score
def get_recall_score(model):
    '''
    model : classifier to predict values of X

    '''
    pred_train = model.predict(X_train)
    pred_test = model.predict(X_test)
    print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
    print("Recall on test set : ",metrics.recall_score(y_test,pred_test))


make_confusion_matrix(dTree,y_test)


decision_tree_perf_train = model_performance_classification_sklearn(
    dTree, X_train, y_train
)
decision_tree_perf_train


confusion_matrix_sklearn(dTree, X_test, y_test)


decision_tree_perf_test = model_performance_classification_sklearn(dTree, X_test, y_test)
decision_tree_perf_test


feature_names = list(X_train.columns)
importances = dTree.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1, class_weight="balanced")

# Grid of parameters to choose from
parameters = {
   "max_depth": np.arange(2,8,2),  # Testing deeper trees in addition to current
    "max_leaf_nodes": [50, 100, 150, 250, 500],  # Adding more choices, including larger values
    "min_samples_split": [10, 30, 50, 100],  # Adjusting the range of splits
    "min_samples_leaf": [1, 5, 10, 20],  # Controlling leaf size, smaller values give finer trees
    "criterion": ["gini", "entropy"],  # Testing both Gini and Entropy for better splits
    # "max_features": [None, "sqrt", "log2"],  # Testing the number of features to consider at each split
    "min_impurity_decrease": [0.0, 0.01, 0.05],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(f1_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data.
estimator.fit(X_train, y_train)

DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
                       min_samples_split=10, random_state=1)

DecisionTreeClassifier(class_weight='balanced', max_depth=6, max_leaf_nodes=50,
                       min_samples_split=10, random_state=1)


confusion_matrix_sklearn(estimator, X_train, y_train)


decision_tree_tune_perf_train = model_performance_classification_sklearn(dTree, X_train, y_train)
decision_tree_tune_perf_train


confusion_matrix_sklearn(estimator, X_test, y_test)


decision_tree_tune_perf_test = model_performance_classification_sklearn(estimator, X_test, y_test)
decision_tree_tune_perf_test


plt.figure(figsize=(20, 10))
out = tree.plot_tree(
    estimator,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of a decision tree -
print(tree.export_text(estimator, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |--- avg_price_per_room <= 202.00
|   |   |   |   |   |   |--- weights: [2356.56, 293.02] class: 0
|   |   |   |   |   |--- avg_price_per_room >  202.00
|   |   |   |   |   |   |--- weights: [1.49, 21.37] class: 1
|   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- weights: [104.11, 157.19] class: 1
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- weights: [350.25, 83.94] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- weights: [245.40, 512.78] class: 1
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- weights: [43.13, 4.58] class: 0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- avg_price_per_room <= 89.50
|   |   |   |   |   |   |--- weights: [169.55, 27.47] class: 0
|   |   |   |   |   |--- avg_price_per_room >  89.50
|   |   |   |   |   |   |--- weights: [124.93, 108.36] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 9.50
|   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |--- avg_price_per_room <= 202.50
|   |   |   |   |   |   |--- weights: [408.25, 93.09] class: 0
|   |   |   |   |   |--- avg_price_per_room >  202.50
|   |   |   |   |   |   |--- weights: [0.74, 16.79] class: 1
|   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- weights: [171.03, 228.92] class: 1
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- weights: [121.96, 21.37] class: 0
|   |   |   |--- lead_time >  9.50
|   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |--- weights: [145.75, 27.47] class: 0
|   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |--- weights: [37.93, 144.98] class: 1
|   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [944.41, 3645.96] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [43.87, 3.05] class: 0
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |--- no_of_week_nights <= 11.00
|   |   |   |   |   |   |--- weights: [699.01, 9.16] class: 0
|   |   |   |   |   |--- no_of_week_nights >  11.00
|   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |--- weights: [5.21, 6.10] class: 1
|   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |--- weights: [78.08, 13.74] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |--- weights: [661.83, 61.05] class: 0
|   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- weights: [2618.32, 1460.52] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [137.57, 1.53] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 89.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1593.60, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- weights: [235.73, 62.57] class: 0
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- weights: [0.00, 6.10] class: 1
|   |   |   |--- lead_time >  89.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- weights: [180.70, 51.89] class: 0
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- weights: [118.24, 99.20] class: 0
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [69.16, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.50
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- weights: [5.21, 32.05] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- weights: [254.32, 68.68] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |   |--- weights: [5.95, 4.58] class: 0
|   |   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |   |--- weights: [1.49, 90.04] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- weights: [216.40, 399.85] class: 1
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- weights: [2.97, 312.86] class: 1
|   |   |   |   |--- avg_price_per_room >  82.50
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- weights: [20.08, 1106.46] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |--- weights: [46.11, 12.21] class: 0
|   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- weights: [14.87, 4.58] class: 0
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- weights: [17.85, 204.50] class: 1
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |--- weights: [107.83, 1.53] class: 0
|   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |--- weights: [8.92, 6.10] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 99.50
|   |   |   |   |   |   |--- weights: [248.37, 137.35] class: 0
|   |   |   |   |   |--- avg_price_per_room >  99.50
|   |   |   |   |   |   |--- weights: [0.00, 19.84] class: 1
|   |--- avg_price_per_room >  100.50
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 3101.13] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [24.54, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [40.16, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |--- weights: [6.69, 27.47] class: 1


# importance of features in the tree building

importances = estimator.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
    clf.fit(X_train, y_train)
    clfs.append(clf)
print("Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
  clfs[-1].tree_.node_count, ccp_alphas[-1]))

Number of nodes in the last tree is: 3 with ccp_alpha: 0.03404622796578144


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


recall_train = [recall_score]
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train)
    recall_train.append(values_train)


recall_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = recall_score(y_test, pred_test)
    recall_test.append(values_test)


train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


f1_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = f1_score(y_train, pred_train)
    f1_train.append(values_train)

f1_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = f1_score(y_test, pred_test)
    f1_test.append(values_test)


fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("F1 Score")
ax.set_title("F1 Score vs alpha for training and testing sets")
ax.plot(ccp_alphas, f1_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, f1_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()


index_best_model = np.argmax(f1_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=9.207759232287106e-05, class_weight='balanced',
                       random_state=1)


confusion_matrix_sklearn(best_model, X_train, y_train)


decision_tree_post_perf_train = model_performance_classification_sklearn(
    best_model, X_train, y_train
)
decision_tree_post_perf_train


confusion_matrix_sklearn(best_model, X_test, y_test)


decision_tree_post_perf_test = model_performance_classification_sklearn(
    best_model, X_test, y_test
)
decision_tree_post_perf_test


plt.figure(figsize=(20, 10))

out = tree.plot_tree(
    best_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |--- avg_price_per_room <= 202.00
|   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.00
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [54.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [38.67, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [130.88, 0.00] class: 0
|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1226.99, 1.53] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 48.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 46.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [23.05, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  46.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  48.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 67.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [162.86, 7.63] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  67.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [20.08, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 121.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 10.68] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  121.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.13, 6.10] class: 0
|   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |--- lead_time <= 78.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.69, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 69.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  69.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  78.50
|   |   |   |   |   |   |   |   |   |--- weights: [46.85, 6.10] class: 0
|   |   |   |   |   |--- avg_price_per_room >  202.00
|   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |--- weights: [0.00, 21.37] class: 1
|   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.69, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 120.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 46.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [12.64, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  46.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  120.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 5.00
|   |   |   |   |   |   |   |   |--- weights: [72.13, 4.58] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  5.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 32.00
|   |   |   |   |   |   |   |   |--- weights: [0.74, 3.05] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  32.00
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [232.76, 12.21] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.05] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [29.75, 6.10] class: 0
|   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.79] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [56.52, 4.58] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 79.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.41, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  79.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |--- weights: [0.74, 19.84] class: 1
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- avg_price_per_room <= 90.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 75.50
|   |   |   |   |   |   |   |   |--- lead_time <= 98.50
|   |   |   |   |   |   |   |   |   |--- weights: [22.31, 4.58] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  98.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.95, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.85, 4.58] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 12.21] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  75.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [52.05, 3.05] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 103.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.26] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  103.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- avg_price_per_room >  90.50
|   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.95, 12.21] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.13, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  97.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 76.31] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.92, 144.98] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |--- weights: [37.93, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |--- weights: [5.21, 4.58] class: 0
|   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |--- avg_price_per_room <= 89.50
|   |   |   |   |   |   |--- lead_time <= 126.50
|   |   |   |   |   |   |   |--- weights: [33.46, 13.74] class: 0
|   |   |   |   |   |   |--- lead_time >  126.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [134.60, 10.68] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.49, 3.05] class: 1
|   |   |   |   |   |--- avg_price_per_room >  89.50
|   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 115.00
|   |   |   |   |   |   |   |   |--- weights: [58.00, 1.53] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  115.00
|   |   |   |   |   |   |   |   |--- weights: [3.72, 4.58] class: 1
|   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [14.87, 6.10] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.39, 51.89] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  23.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 36.63] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |--- weights: [31.23, 1.53] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.10] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 9.50
|   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |--- avg_price_per_room <= 202.50
|   |   |   |   |   |   |--- avg_price_per_room <= 60.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 59.50
|   |   |   |   |   |   |   |   |--- weights: [25.28, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  59.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 22.89] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  60.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [50.57, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.05, 4.58] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  12.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 13.74] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |--- avg_price_per_room >  202.50
|   |   |   |   |   |   |--- weights: [0.74, 16.79] class: 1
|   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- avg_price_per_room <= 96.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [30.49, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.41, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.82, 24.42] class: 1
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.46, 13.74] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  96.50
|   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 102.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  102.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.13, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 13.74] class: 1
|   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [15.62, 102.25] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.68] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 124.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.21, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  124.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.95, 16.79] class: 1
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [29.75, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.05] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.33, 4.58] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 6.10] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [69.90, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |--- weights: [0.00, 7.63] class: 1
|   |   |   |--- lead_time >  9.50
|   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |--- avg_price_per_room <= 212.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 115.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |--- weights: [124.93, 9.16] class: 0
|   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  115.50
|   |   |   |   |   |   |   |   |--- weights: [20.82, 9.16] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  212.00
|   |   |   |   |   |   |   |--- weights: [0.00, 7.63] class: 1
|   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [26.03, 85.46] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.58] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |--- weights: [7.44, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 85.00
|   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  85.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 54.94] class: 1
|   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 104.50
|   |   |   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [39.41, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 25.94] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [66.18, 128.20] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [48.34, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 55.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 3.05] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.80, 1.53] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  55.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |--- avg_price_per_room >  104.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 194.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 130.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  130.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [143.52, 1045.41] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  194.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 125.14] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.41, 4.58] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 7.63] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- lead_time <= 23.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.63] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [17.85, 1.53] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  23.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.21, 61.05] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.69, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 6.10] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 9.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 209.50
|   |   |   |   |   |   |   |   |--- weights: [43.87, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  209.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  9.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |--- no_of_week_nights <= 11.00
|   |   |   |   |   |   |--- weights: [699.01, 9.16] class: 0
|   |   |   |   |   |--- no_of_week_nights >  11.00
|   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |--- lead_time <= 105.00
|   |   |   |   |   |   |--- avg_price_per_room <= 74.50
|   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  74.50
|   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 6.10] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  105.00
|   |   |   |   |   |   |--- weights: [78.08, 13.74] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_week_nights <= 10.00
|   |   |   |   |   |   |   |--- weights: [522.77, 30.52] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  10.00
|   |   |   |   |   |   |   |--- weights: [0.74, 3.05] class: 1
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.67, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.58] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.36, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- weights: [18.59, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |--- weights: [69.16, 4.58] class: 0
|   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 117.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 61.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [73.62, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [136.83, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  61.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 108.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.97, 30.52] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  108.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 136.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  136.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  117.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 141.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  141.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.36, 10.68] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [36.44, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 15.26] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |--- weights: [137.57, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 89.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1593.60, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [37.93, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.80, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [19.33, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [55.77, 0.00] class: 0
|   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |--- weights: [0.00, 6.10] class: 1
|   |   |   |--- lead_time >  89.50
|   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- weights: [0.74, 15.26] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- weights: [8.18, 3.05] class: 0
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 201.50
|   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [58.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.58] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  201.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 9.16] class: 1
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- lead_time <= 93.50
|   |   |   |   |   |   |   |--- lead_time <= 90.50
|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  90.50
|   |   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.16] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 92.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  92.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.58] class: 1
|   |   |   |   |   |   |--- lead_time >  93.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 169.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 78.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.36, 3.05] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  78.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 87.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.21, 10.68] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  87.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  169.00
|   |   |   |   |   |   |   |   |   |--- weights: [7.44, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 117.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.18, 1.53] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 6.10] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  117.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.68] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |--- weights: [69.16, 0.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.50
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- avg_price_per_room <= 85.50
|   |   |   |   |   |   |   |--- weights: [5.21, 1.53] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  85.50
|   |   |   |   |   |   |   |--- weights: [0.00, 30.52] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- lead_time <= 165.00
|   |   |   |   |   |   |   |   |   |--- weights: [52.05, 12.21] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  165.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 16.79] class: 1
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 13.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 6.10] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  13.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.21, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [182.93, 4.58] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.74, 6.10] class: 1
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 12.21] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |--- weights: [9.67, 10.68] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 2.50
|   |   |   |   |   |   |--- lead_time <= 280.00
|   |   |   |   |   |   |   |--- weights: [5.95, 1.53] class: 0
|   |   |   |   |   |   |--- lead_time >  280.00
|   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |--- avg_price_per_room >  2.50
|   |   |   |   |   |   |--- weights: [1.49, 90.04] class: 1
|   |   |   |--- no_of_adults >  1.50
|   |   |   |   |--- avg_price_per_room <= 82.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- lead_time <= 241.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.00
|   |   |   |   |   |   |   |   |   |--- weights: [16.36, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 170.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  170.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 54.94] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.92, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 191.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  191.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 12.21] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [89.98, 10.68] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 3.05] class: 1
|   |   |   |   |   |   |--- lead_time >  241.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- weights: [29.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.69, 259.44] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 273.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  273.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [26.03, 0.00] class: 0
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- weights: [2.97, 312.86] class: 1
|   |   |   |   |--- avg_price_per_room >  82.50
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- lead_time <= 325.50
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.18, 1060.67] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.21] class: 1
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 22.89] class: 1
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.74, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  325.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 10.68] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [4.46, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |--- lead_time <= 156.50
|   |   |   |   |   |   |   |--- weights: [4.46, 6.10] class: 1
|   |   |   |   |   |   |--- lead_time >  156.50
|   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [1.49, 3.05] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [37.93, 0.00] class: 0
|   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 356.00
|   |   |   |   |   |   |   |   |--- weights: [14.87, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  356.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.53] class: 1
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 3.05] class: 1
|   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 33.50
|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  33.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 186.19] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 276.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 221.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.74, 4.58] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  221.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.21, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  276.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.49, 13.74] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [8.18, 0.00] class: 0
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |--- weights: [107.83, 1.53] class: 0
|   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |--- weights: [5.95, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |--- lead_time <= 184.00
|   |   |   |   |   |   |   |   |--- weights: [0.74, 6.10] class: 1
|   |   |   |   |   |   |   |--- lead_time >  184.00
|   |   |   |   |   |   |   |   |--- weights: [2.23, 0.00] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 99.50
|   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |--- lead_time <= 356.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 92.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  92.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.44, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.72, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.44, 13.74] class: 1
|   |   |   |   |   |   |   |--- lead_time >  356.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.58] class: 1
|   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [10.41, 3.05] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.00
|   |   |   |   |   |   |   |   |   |--- weights: [5.21, 1.53] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.18, 29.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  99.50
|   |   |   |   |   |   |--- weights: [0.00, 19.84] class: 1
|   |--- avg_price_per_room >  100.50
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 3101.13] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [24.54, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [40.16, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |--- weights: [2.97, 0.00] class: 0
|   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |--- weights: [6.69, 27.47] class: 1


importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train.T,
        decision_tree_tune_perf_train.T,
        decision_tree_post_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison
models_test_comp_df = pd.concat(
    [
        decision_tree_perf_test.T,
        decision_tree_tune_perf_test.T,
        decision_tree_post_perf_test.T,],
    axis=1,
)
models_test_comp_df.columns = [
    "Decision Tree sklearn",
    "Decision Tree (Pre-Pruning)",
    "Decision Tree (Post-Pruning)",
]
print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:

	count	mean	std	min	25%	50%	75%	max
no_of_adults	36275.00000	1.84496	0.51871	0.00000	2.00000	2.00000	2.00000	4.00000
no_of_children	36275.00000	0.10528	0.40265	0.00000	0.00000	0.00000	0.00000	10.00000
no_of_weekend_nights	36275.00000	0.81072	0.87064	0.00000	0.00000	1.00000	2.00000	7.00000
no_of_week_nights	36275.00000	2.20430	1.41090	0.00000	1.00000	2.00000	3.00000	17.00000
required_car_parking_space	36275.00000	0.03099	0.17328	0.00000	0.00000	0.00000	0.00000	1.00000
lead_time	36275.00000	85.23256	85.93082	0.00000	17.00000	57.00000	126.00000	443.00000
arrival_year	36275.00000	2017.82043	0.38384	2017.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	36275.00000	7.42365	3.06989	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	36275.00000	15.59700	8.74045	1.00000	8.00000	16.00000	23.00000	31.00000
repeated_guest	36275.00000	0.02564	0.15805	0.00000	0.00000	0.00000	0.00000	1.00000
no_of_previous_cancellations	36275.00000	0.02335	0.36833	0.00000	0.00000	0.00000	0.00000	13.00000
no_of_previous_bookings_not_canceled	36275.00000	0.15341	1.75417	0.00000	0.00000	0.00000	0.00000	58.00000
avg_price_per_room	36275.00000	103.42354	35.08942	0.00000	80.30000	99.45000	120.00000	540.00000
no_of_special_requests	36275.00000	0.61966	0.78624	0.00000	0.00000	0.00000	1.00000	5.00000

	booking_status
13662	1
26641	0
17835	0
21485	1
5670	1

	const	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	repeated_guest	no_of_previous_cancellations	avg_price_per_room	no_of_special_requests	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Corporate	market_segment_type_Offline
Odds	0.00000	1.11594	1.16534	1.11553	1.04319	0.20297	1.01584	1.57088	0.95834	0.06471	1.25716	1.01938	0.23000	1.17746	1.32950	0.70275	0.75348	0.47952	0.37976	0.23833	0.45111	0.16695
Change_odd%	-100.00000	11.59416	16.53413	11.55280	4.31861	-79.70273	1.58437	57.08751	-4.16607	-93.52885	25.71559	1.93804	-77.00048	17.74556	32.94984	-29.72500	-24.65213	-52.04826	-62.02421	-76.16674	-54.88875	-83.30520

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80557	0.79289	0.80139
Recall	0.63303	0.73419	0.69891
Precision	0.73918	0.66914	0.69833
F1	0.68200	0.70015	0.69862

	Logistic Regression-default Threshold	Logistic Regression-0.37 Threshold	Logistic Regression-0.42 Threshold
Accuracy	0.80447	0.79629	0.80327
Recall	0.63118	0.73822	0.70216
Precision	0.72837	0.66752	0.69369
F1	0.67630	0.70109	0.69790

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00000	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68000	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00000	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00000	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50000	0	Canceled
5	INN00006	2	0	2	Meal Plan 2	Room_Type 1	346	2018	9	13	Online	115.00000	1	Canceled
6	INN00007	2	1	3	Meal Plan 1	Room_Type 1	34	2017	10	15	Online	107.55000	1	Not_Canceled
7	INN00008	2	1	3	Meal Plan 1	Room_Type 4	83	2018	12	26	Online	105.61000	1	Not_Canceled
8	INN00009	3	0	4	Meal Plan 1	Room_Type 1	121	2018	7	6	Offline	96.90000	1	Not_Canceled
9	INN00010	2	0	5	Meal Plan 1	Room_Type 4	44	2018	10	18	Online	133.44000	3	Not_Canceled

	0
Booking_ID	0
no_of_adults	0
no_of_children	0
no_of_weekend_nights	0
no_of_week_nights	0
type_of_meal_plan	0
required_car_parking_space	0
room_type_reserved	0
lead_time	0
arrival_year	0
arrival_month	0
arrival_date	0
market_segment_type	0
repeated_guest	0
no_of_previous_cancellations	0
no_of_previous_bookings_not_canceled	0
avg_price_per_room	0
no_of_special_requests	0
booking_status	0

	count
arrival_month
10	5317
9	4611
8	3813
6	3203
12	3021
11	2980
7	2920
4	2736
5	2598
3	2358
2	1704
1	1014

	count
market_segment_type
Online	23214
Offline	10528
Corporate	2017
Complementary	391
Aviation	125

	feature	VIF
0	const	39490734.57350
27	market_segment_type_Online	71.17803
26	market_segment_type_Offline	64.11618
25	market_segment_type_Corporate	16.92839
24	market_segment_type_Complementary	4.50251
2	no_of_children	2.09343
13	avg_price_per_room	2.06016
22	room_type_reserved_Room_Type 6	2.05560
10	repeated_guest	1.78355
12	no_of_previous_bookings_not_canceled	1.65200
7	arrival_year	1.43165
11	no_of_previous_cancellations	1.39569
6	lead_time	1.39537
20	room_type_reserved_Room_Type 4	1.36326
1	no_of_adults	1.35066
8	arrival_month	1.27625
15	type_of_meal_plan_Meal Plan 2	1.27330
17	type_of_meal_plan_Not Selected	1.27270
14	no_of_special_requests	1.24798
23	room_type_reserved_Room_Type 7	1.11801
18	room_type_reserved_Room_Type 2	1.10605
4	no_of_week_nights	1.09595
3	no_of_weekend_nights	1.06974
5	required_car_parking_space	1.03998
21	room_type_reserved_Room_Type 5	1.02791
16	type_of_meal_plan_Meal Plan 3	1.02526
9	arrival_date	1.00679
19	room_type_reserved_Room_Type 3	1.00330

	ccp_alphas	impurities
0	0.00000	0.00833
1	-0.00000	0.00833
2	0.00000	0.00833
3	0.00000	0.00833
4	0.00000	0.00833
...	...	...
1636	0.00880	0.32791
1637	0.00941	0.33732
1638	0.01253	0.34985
1639	0.03405	0.41794
1640	0.08206	0.50000

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.99437	0.99437	0.91714
Recall	0.98570	0.98570	0.93148
Precision	0.99708	0.99708	0.83475
F1	0.99136	0.99136	0.88047

	Decision Tree sklearn	Decision Tree (Pre-Pruning)	Decision Tree (Post-Pruning)
Accuracy	0.86355	0.83451	0.86033
Recall	0.79641	0.77987	0.84212
Precision	0.78911	0.73242	0.75833
F1	0.79274	0.75540	0.79803

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Data Overview¶

Exploratory Data Analysis (EDA)¶

Univariate Analysis¶

Observations on average price per room¶

Bivariate Analysis¶

Data Preprocessing¶

EDA¶

Bivariate Analysis¶

Model Building¶

Model evaluation criterion¶

Model can make wrong predictions as:¶

Which case is more important?¶

How to reduce the losses?¶

First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.¶

Logistic Regression (with statsmodels library)¶

Data Preparation for modeling (Logistic Regression)¶

Building Logistic Regression Model¶

Multicollinearity¶

Dropping high p-value variables¶

Overall Comparison:¶

Converting coefficients to odds¶

ROC-AUC¶

Model Performance Improvement¶

Optimal threshold using AUC-ROC curve¶

Let's use Precision-Recall curve and see if we can find a better threshold¶

Checking model performance on training set¶

Observations from Logistic Regression model:¶

Decision Tree¶

First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.¶

Checking model performance on training set¶

Checking model performance on test set¶

Pruning the tree¶

Checking performance on training set¶

Checking performance on test set¶

Visualizing the Decision Tree¶

Decision Tree (Post Pruning)¶

F1 Score vs alpha for training and testing sets¶

Checking performance on training set¶

Checking performance on test set¶

Actionable Insights and Recommendations¶